Integrated medical platform

ABSTRACT

Provided is a system for automated medical decision-making. The system may include a first parser configured to parse text associated with medical information sources to obtain medical information and a second parser configured to parse patient data to obtain processed patient data. A processor, in communication with the first parser and the second parser, is configured to structure the medical information to form structured medical metadata in an intelligent medical database. Based on the structured medical metadata, the processor creates a causal network and receives the patient data from patient data sources. When the patient data is parsed by the second parser and the processed patient data is obtained, the processor maps the processed patient data against the causal network and generates the medical decision for the patient based on the mapping.

TECHNICAL FIELD

The application relates generally to the field of medicine and, morespecifically, to automated medical decision-making.

BACKGROUND

Recent decades have seen considerable advancements in bio-informaticsand artificial intelligence in medicine. Various computerized expertsystems have been developed to collate medical data with varied diseasesto aid physicians in diagnosing and prescribing treatments for theirpatients. Numerous systems exist that provide diagnoses or suggesttreatment strategies.

However, the knowledge base for medicine is vast and varied. There aredisparate libraries, books, articles, and papers that describe variousaspects of medical knowledge written in natural languages. Even whereinformation about a specific disease is collated and provided in acomprehensive document, there is no mechanism by which this document canbe immediately utilized to diagnose a disease without a careful review.

The ability to make intelligent conclusions based on the availableknowledge is a difficult task, owing to the complex informatics,inferential, and value-laden nature and character of medicaldecision-making.

SUMMARY

Provided are methods and systems for automated medical decision-making.In general, the disclosed methods and systems are related to dataparsing and automated knowledge discovery, bioinformatics ontology,diagnostic reasoning coupled with machine learning, decision-making, andrisk-based evaluation. In the present disclosure, an integrated medicalplatform is provided that assists, particularly in an onlineenvironment, a patient or attending physician in determining possiblediagnoses with accompanying statistical likelihoods, complete withrecommended treatment and patient management plans. Additionally, thedisclosed methods and systems can facilitate self-monitoring andmanagement of chronic health conditions of the patient.

In certain embodiments, a method for providing automated medicaldecision-making involves parsing, by a first parser, text associatedwith one or more medical information sources to obtain medicalinformation, and structuring the medical information to form structuredmedical metadata in an intelligent medical database. The structuredmedical metadata may comprise elements and relationships between theelements. The elements of the structured medical metadata may includeone or more of the following: a disease, a state, a cause, a symptom, atreatment, a test, an effect, an outcome, or an outlier. A causalnetwork may be created based on the structured medical metadata. Thecausal network may include a topological space with nodes and linksbetween the nodes, wherein the nodes represent the elements of thestructured medical metadata, and the links represent the relationshipsbetween the elements. The relationships between the elements of thestructured medical metadata may be weighted statistically based on thepatient data. The method may further involve receiving patient data fromone or more patient data sources; parsing, by a second parser (e.g.,alternative bio-informatics ontology), the patient data to obtainprocessed patient data; mapping the processed patient data against thecausal network; and, based on the mapping, providing automated medicaldecision-making, such as generating a medical decision for a patient.

In certain embodiments, the first parser and the second parser mayinclude grammar independent natural language parsers configured toretrieve, interpret, and collate the medical information from the one ormore medical information sources.

In certain embodiments, the medical information may be obtained from theone or more medical information sources using a metadata dictionaryand/or pattern recognition. The metadata dictionary may include anequivalence class of terms and phrases associated with a medicallexicon. The metadata dictionary may be captured using bioinformaticsontology.

In certain embodiments, the patient data may be processed usinginference model and pattern recognition. The patient data may includepatient input in the form of a natural language. The patient input mayinclude text or speech converted into text.

BRIEF DESCRIPTION OF THE DRAWINGS

Embodiments are illustrated by way of example and not limitation in thefigures of the accompanying drawings.

FIG. 1 illustrates a block diagram showing a sample environment withinwhich methods and systems for automated medical decision-making may beimplemented.

FIG. 2 shows a block diagram illustrating a sample system for automatedmedical decision-making.

FIG. 3 shows a flow chart illustrating a method for automated medicaldecision-making.

FIG. 4 illustrates a sample causal network.

FIG. 5 illustrates a sample screen for a metadata library.

FIG. 6 illustrates a process of decision making using the system forautomated medical decision-making.

FIG. 7 illustrates creating a patient substrate using the system forautomated medical decision-making.

FIG. 8 illustrates finding a match based on symptoms and evidences.

FIG. 9 illustrates a more detailed procedure for finding a match basedon symptoms and evidences.

FIG. 10 illustrates a diagrammatic representation of an example machinein the form of a computer system within which a set of instructions forcausing the machine to perform any one or more of the methodologiesdiscussed herein is executed.

DETAILED DESCRIPTION

In the following description, numerous specific details are set forth inorder to provide a thorough understanding of the presented concepts. Thepresented concepts may be practiced without some or all of thesespecific details. In other instances, well known process operations havenot been described in detail so as to not unnecessarily obscure thedescribed concepts. While some concepts will be described in conjunctionwith the specific embodiments, it will be understood that theseembodiments are not intended to be limiting.

Systems and methods described herein may allow a user of a computingdevice, such as a desktop computer, laptop computer, cell phone, smartphone, or the like, to receive a diagnosis and treatment option, healthconditions of a certain person, medical history, and other (possiblyrelevant) environmental factors. The diagnosis and treatment option maybe provided by an integrated medical platform. The integrated medicalplatform may be part of the system for automated medicaldecision-making. The integrated medical platform may be based oninference and statistics instead of, or in addition to, pathology orclinical gestalt. The integrated medical platform may include twonatural language parsers, each with unique methods and functions. Thefirst (backend) parser may be responsible for reading various medicalliterature (journals, standards, and the like) and building backendstructures that form the framework of the system intelligence. Thesecond (frontend) parser may read electronic medical records (EMRs) andparse patient input, taking pertinent information and feeding it to thebackend structures. These structures may include subsystems of anintelligent medical database, which stores all relevant informationpertaining to various diseases in a well-structured way.

The system may further comprise a causal network, which connects varioussymptoms, treatments, and diseases in ways that a database table couldnot have possibly achieved, and in more ways than any doctor couldremember at any given moment. There may also be provided two distinctinference models: one inference model may be used with the second parserto help in determining a meaning of the patient input, and the other,with the first parser, makes use of the statistical data available toestablish a diagnosis. These subsystems may feed data into theintegrated medical platform that provides, to a patient or attendingphysician, a set of possible diagnoses with accompanying statisticallikelihoods, complete with recommended treatment, patient managementplans, and the ability to project and/or track a treatment prognosis ofa patient. These subsystems can be assisted by a QALY(quality-adjusted-life-years) valuation component.

In certain example embodiments, there may be provided several derivativeproducts that emerge as a direct result of the technology described inthe present application, among which are medical/life insurance relatedmodels, various data products (efficiency of treatment options,comparative analysis between drugs), specific patient-centric forecastmodels, unique scheduling algorithms (obtaining tests needed fordiagnosis before visiting a doctor), and unique advertising for drugcompanies, as well as doctor tracking. For example, doctors whoserecommendations consistently contradict the recommendations of thesystem, resulting in patients with treatable illnesses returning morefrequently than they should, can be reviewed by the patient andemploying clinics.

The integrated medical platform of the present technology may haveseveral advantages: 1) all potential diseases associated with anypatient and set of symptoms are considered; 2) the probability of eachdisease is calculated; 3) outliers, in particular dangerous ones, areidentified; 4) missed questions with respect to symptoms areautomatically asked to render the best statistical diagnosis possible;5) tests that enhance the probability of a condition, sometimes inadvance of a visit of a doctor; 6) main disadvantages of clinicalgestalt are obviated; 7) the entirety of medical clinical gestalt isalways considered; 8) efficiency in traversing the network as opposed toreading through tables; 9) immediate availability of probabilitydistributions assigned to information.

The integrated medical platform may receive the information from thefirst and second parsers, identify medical metadata and phrases, andinsert the metadata and phrases into the causal network, typically, assymptoms, if coming directly from a patient. If the information iscoming from the EMR of the patient, the integrated medical platform mayparse the information and create a clinical gestalt of the patient as asubnet of the causal network. The subnet of the causal network maycomprise nodes that represent diseases, symptoms, treatments, statesassociated with the patient, and links between the nodes that aredirected dependencies. The links in the causal network subnet may bestatistically weighted, and these weights may be specific to thepatient. The second parser may feed symptoms and other data to theintegrated medical platform. The integrated medical platform may map thesymptoms against the causal network and identify the entire subnet forthese symptoms. This approach will allow recognizing a set of diseases,conditions, and states together with corresponding probabilities.Probability distributions may be assigned dynamically to a patient,based on the characteristics of the patient, the probabilitydistributions of the causal network, and the statistical database forthe patient population.

The integrated medical platform may provide the capability of traversingthe causal network in multiple directions or paths, thereby identifyingadditional or corroborating symptoms, conditions, treatments, and states(amongst other relationships) along with corresponding likelihoods. Inparticular, for each potential disease or condition identified,associated diseases, symptoms and outcomes can be calculated andlikelihood functions assigned (e.g., the likely condition of the patientand the likely responses to a given set of treatment options). If thelikelihood falls below a certain threshold, the integrated medicalplatform may not render a medical decision or diagnosis.

The integrated medical platform may build a list of diseases orconditions and refine a medical decision or conclusion by askingquestions or by requesting specific tests. This refinement, or set ofrefinements, may be established to eliminate dangerous outcomes,outliers, and to put aside other tests or investigations that do notincrease the likelihood of the diagnosis.

A medical decision may be essentially a subnet of the causal networkwith a probability distribution for the subnet. The subnet may containmultiple diagnoses, which may be similar or different. The medicaldecision may be the most likely set of concurrent discrete paths in thesubnet. The integrated medical platform may be developed with severalunique methods for understanding severity and acuteness of patientconditions. Severity and acuteness of patient conditions has always beendifficult to establish due to the subjectivity of the patient responseand the absence of reliable measurements.

The integrated medical platform may have additional capability forinterpreting pain levels from the perspective of a disease or condition.For example, if the pain is likely associated with appendicitis, theintegrated medical platform may have many options for the location(s)and acuteness of the pain.

Having determined a diagnosis in the earlier steps, the system forautomated medical decision-making may now turn to patient care. Today,when a doctor prescribes a treatment that works, the patient does notreturn. If, on the other hand, the treatment does not work, the patientwill be back and his condition may even get worse.

The system should know, within statistical boundaries, how the patientis responding to medication/care over the course of the treatment. Bygetting constant feedback from the patient over the course of his/herregimen, doctors, and patients can quickly determine whether the correctmedical decision and/or treatment option was made. If, for example, apatient who apparently suffers from asthma at night only was placed on atreatment plan for acid reflux disease, and after a week the patient nolonger experiences any improvements, the system may reevaluate themedical decision/treatment and adjust accordingly.

Moreover, the system may be able to provide the patient with a prognosiswithin statistical boundaries of the results, risks, and consequences oftreatment or lack thereof, thus allowing the patient/doctor to act on afull knowledge set and come to a conclusion about the best way forwardfor the patient given his or her circumstances.

Using the causal network and corresponding probability assignments, thesystem allows predicting future patient gestalts, identifying diseasesand treatments, progression and consequences the diseases will have overtime for a patient, and possible treatments. Using the causal networkand the statistical data from patient population, more accurateprediction of drug use and other treatment options may be made.Additionally, mortality and morbidity predictions, with and withouttreatment, may be made. Screening requirements for individuals withinthe patient population may be determined from the statistical tables andpreventative measures taken.

Referring now to the drawings, FIG. 1 shows an architecture 100 that mayinclude a network 110, client devices 120, users 125, a user interface115, a system for automated medical decision-making 200, and anintelligent medical database 105 which is part of the system forautomated medical decision-making 200. The network 110 may include theInternet or any other network capable of communicating data betweendevices. Suitable networks may include or interface with any one or moreof, for instance, a local intranet, a PAN (Personal Area Network), a LAN(Local Area Network), a WAN (Wide Area Network), a MAN (MetropolitanArea Network), a virtual private network (VPN), a storage area network(SAN), a frame relay connection, an Advanced Intelligent Network (AIN)connection, a synchronous optical network (SONET) connection, a digitalT1, T3, E1 or E3 line, Digital Data Service (DDS) connection, DSL(Digital Subscriber Line) connection, an Ethernet connection, an ISDN(Integrated Services Digital Network) line, a dial-up port such as aV.90, V.34 or V.34bis analog modem connection, a cable modem, an ATM(Asynchronous Transfer Mode) connection, or an FDDI (Fiber DistributedData Interface) or CDDI (Copper Distributed Data Interface) connection.Furthermore, communications may also include links to any of a varietyof wireless networks, including WAP (Wireless Application Protocol),GPRS (General Packet Radio Service), GSM (Global System for MobileCommunication), CDMA (Code Division Multiple Access) or TDMA (TimeDivision Multiple Access), cellular phone networks, GPS (GlobalPositioning System), CDPD (cellular digital packet data), RIM (Researchin Motion, Limited) duplex paging network, Bluetooth radio, or an IEEE802.11-based radio frequency network. The network 110 can furtherinclude or interface with any one or more of an RS-232 serialconnection, an IEEE-1394 (Firewire) connection, a Fiber Channelconnection, an IrDA (infrared) port, a SCSI (Small Computer SystemsInterface) connection, a USB (Universal Serial Bus) connection or otherwired or wireless, digital or analog interface or connection, mesh orDigi® networking. The network 110 may include a network of dataprocessing nodes that are interconnected for the purpose of datacommunication.

The client devices 120, in some example embodiments, may include aGraphical User Interface (GUI) for displaying the user interface 115. Ina typical GUI, instead of offering only text menus or requiring typedcommands, the system presents graphical icons, visual indicators, orspecial graphical elements that may be utilized to allow users 125 tointeract with the user interface 115. The client devices 120 may beconfigured to utilize icons used in conjunction with text, labels, ortext navigation to fully represent the information and actions availableto users.

The client devices 120 may include a desktop computer, laptop computer,cell phone, smart phone, gaming device, a wearable device, or the like.The user 125, in some example embodiments, is a person interacting withthe user interface 115 via the client devices 120. The user 125 mayexperience certain symptoms and be seeking medical care. The user 125may access the system for automated medical decision-making 200 througha client device 120 and the Internet. Alternatively, the user 125 mayaccess the system 200 via a mobile or another device. The user 125 mayperiodically interact with the system for providing automated medicaldecision-making 200 and present information to the system for automatedmedical decision-making 200. This information may be stored in anintelligent medical database 105 and include medical knowledge relatingto diseases, states, causes, symptoms, and the like. All medicalknowledge may be expressed as the sum of a set of binary relationshipsbetween elements of the intelligent medical database 105 including butnot limited to: causes, symptoms, treatments, tests, effects, outcomes,diseases, states, and the like, wherein relations between the elementsmay include but be not limited to: causes, effects, conditionallikelihoods, and the like. These relationships may be further qualifiedby sex, age, and the like, which may include various qualities andquantities associated with a patient. For example, smoking has a causalrelationship to emphysema, gastro-esophageal reflux disease (GERD), andasthma. Similarly, a postal code of a patient may have a conditionallikelihood relationship with breast cancer. The intelligent medicaldatabase 105 is a set of binary relationships that forms a cover ofexistent (known) medical knowledge and may include pathologicalrelationships if desired.

Once the data obtained from the user 125 is processed, the user 125 maybe provided with information concerning his or her condition, and arecommendation may be made with respect to further medical care.

FIG. 2 is a block diagram illustrating a sample system for automatedmedical decision-making 200, in accordance with an example embodiment.The sample system 200 may comprise a processor (not shown). Theprocessor may be configured to parse text associated with one or moremedical information sources 205 to obtain medical information, structurethe medical information to form structured medical metadata in anintelligent medical database 105, and create a causal network 240 basedon the structured medical metadata. The structured medical metadata maycomprise elements and relationships between the elements.

The processor may be further configured to receive patient data from oneor more patient data sources 210, parse the patient data to obtainprocessed patient data, and map the processed patient data against thecausal network.

Finally, the processor may be configured to generate a medical decisionfor a patient based on the mapping of the processed patient data againstthe causal network.

The system 200 may also comprise a first parser 215 and a second parser220. The first parser 215 may be used to parse text associated with oneor more medical information sources 205 to obtain medical information.The first parser 215 may be further configured to create one or morebackend structures based on the obtained medical information. The one ormore backend structures may form a framework against which mapping isperformed. The second parser 220 may be used to parse patient data fromone or more patient data sources 210 to obtain processed patient data.

In certain example embodiments, the first parser 215 may use equivalenceclasses of medical terms and phrases found in a metadata dictionary 225.The first parser 215 may then use the metadata dictionary 225, alongwith pattern recognition 230, to pick up pertinent medical informationand map the information against the backend structures, thus populatingthe intelligent medical database 105 with various medical terms andphrases, such as, for example, names of diseases. These medical termsand phrases may be referred to as elements of the intelligent medicaldatabase 105.

In certain example embodiments, the first parser 215 may buildrelationships between the elements of the intelligent medical database105. For example, the first parser 215 may build a relationship betweenthe two diseases asthma and GERD in the intelligent medical database105. The first parser 215 may be concerned with mapping medicalarticles, doctor inputs, and medical journals to the intelligent medicaldatabase 105. Having predefined the concept of a symptom, the firstparser 215 may, using pattern recognition 230, establish the meaning ofthe word or phrase and appropriately place the word or phrase within thestructure of the intelligent medical database 105.

The medical metadata may be constantly updated and appended. The firstparser 215 may read, decode, and update the medical metadata to reflectthe additions and modifications as new journals are published andaccepted by the medical community, new drugs are developed, diseasesdiscovered, treatment data changes, and new standards are constructed,Relationships between diseases previously thought as unrelated may beformalized, and the medical metadata may be updated to reflect thesechanges. The entire process may be auditable at various points bymedical professionals.

In certain example embodiments, the second parser 220 may use aninference model 235, medical vocabulary, and pattern recognition 230 toobtain important information from a patient input and disregard theunimportant information. It is important to note that the second parser220 is not a grammar-based parser. This is imperative for the system forautomated medical decision-making 200 to provide clear reports becausepatients and doctors could be rightfully concerned about a condition andreports that are not communicated clearly. The system 200 can betranslated easily into any language without a complete rewrite of theparser logic.

The second parser 220 may take symptoms, diseases, and medical histories(such as electronic medical records), and build a use case for apatient, prompting for any information that requires attention (forexample sex, age, and the like). This information may provide thecontext for the responses with appropriate pattern recognition andkeywords that are acceptable as a response. The second parser 220 may beprovided with intelligence to understand the relevance of phrasessubmitted. For example, if someone inputs that he or she is “feelingit's difficult to breathe,” the second parser 220 has this phrase listedas a symptom. The second parser may then pick up this phrase and drop itinto a symptom node 250 in a causal network 240, rapidly traversing thecausal network 240 to ascertain all possible diagnoses.

The second parser 220 may be aided by an inference model 235. If apatient inputs “my heart aches for my lost love,” for example, thesystem 200 will disregard the possible romantic context and infer “thepatient is having chest pains.”

The second parser 220 may take natural language and identify what thepatient is describing about himself, his symptoms, and the severity ofthese symptoms based on pattern recognition artificial intelligence 230.There is no grammar base for the parsing, and the system, apart from themetadata and phrase library, is language independent.

The second parser 220 may feed the symptoms and other data to anintegrated medical platform 245. The integrated medical platform 245 maymap these symptoms against the causal network 240 and identify a subnetfor these symptoms. The integrated medical platform 245 may recognize aset of diseases, conditions and states, together with correspondingprobabilities.

The integrated medical platform 245 may include a list of questions and,possibly, tests. For each specific case, the integrated medical platform245 may formulate and immediately ask questions to better rank thepossibilities for each disease. Responses may be refactored into amedical decision and a ranked assessment may be made.

From the initial list with the corresponding probability distributionand ranking, additional symptoms may be identified for each disease orcondition on the list. The integrated medical platform 245 may determinea new set of questions and tests it needs in order to accomplish severalthings, such as: 1) identify or eliminate any serious conditions(sometime outliers); 2) refine the likelihood of any of the rankedconditions identified; 3) shorten the list of possibilities; 4) arriveat a high likelihood for a diagnosis; 5) decide to provide this casedirectly to a doctor because the integrated medical platform 245 has noconfidence in the diagnosis (statistically), and so forth.

FIG. 3 is a process flow diagram, illustrating a method 300 forautomated medical decision-making, in accordance with an exampleembodiment. The method 300 may commence with parsing, by a first parser,a text associated with one or more medical information sources to obtainmedical information, at operation 305. At operation 310, the medicalinformation may be structured to form structured medical metadata in anintelligent medical database. The structured medical metadata maycomprise elements and relationships between the elements. At operation315, a causal network may be created based on the structured medicalmetadata.

The method 300 may proceed with receiving patient data from one or morepatient data sources, at operation 320, and parsing, by a second parser,the patient data to obtain processed patient data, at operation 325. Theprocessed patient data may further be mapped against the causal network,at operation 330, and, based on the mapping, the medical decision may begenerated for a patient, at operation 335.

FIG. 4 is a sample causal network, in accordance with an exampleembodiment. The causal network 400 is a unique morphology for storingand representing medical knowledge that imbues a unique behavior. Thecausal network 400 may display a rich set of characteristics, whichsubstantially enhance the clinical gestalt of diagnosis. The causalnetwork 400 may show a potential totality of medical conditions, causes,symptoms, treatments, outcomes, and other relationships, together withthe probability distribution of such relationships based on theexperience of a population of patients and knowledge embedded into thesystem for providing automated medical decision-making 200.

The causal network 400 may be a path connected topological spacecomprising nodes 405 that represent diseases (Disease A, Disease Y,Disease Z), symptoms (Symptom X, Symptom Y), treatments, states (StateX), and the like and relationships 410 between the nodes 405 that aredirected dependencies. The relationships 410 in the causal network 400are statistically weighted, and these weights are unique per patient andeven per use for the same patient as a case history for that patient mayinfluence the probabilities of the causal network 400.

The nodes 405 and relationships 410 of the causal network 400 may form apattern recognition natural language for medical knowledge. The causalnetwork 400 may define medical metadata, the relationships 410 that mayexist between metadata elements, and the manner in which they canreference each other.

The structure of the causal network 400 may be characterized by nodes405 representing the elements of the intelligent medical database. Therelationships 410 between the nodes 405 may be characterized byrelationships among the elements, with the added property that therelationships 410 may be weighted statistically (dynamically) based onpatient profile, patient data, and other population data.

In certain example embodiments, building the causal network 400 mayinclude mapping the relationships into an adjacency matrix, and theadjacency matrix may be used to construct a path matrix.

The causal network 400 may represent the ways in which the elements ofthe intelligent medical database can relate to each other. For example,symptoms may be related to diseases, diseases may be related tooutcomes, symptoms may be related to outcomes, diseases may be relatedto treatments, and so forth.

Furthermore, the nature of the relationships 410 can carry additionalinformation and qualifications, such as likelihood or severity. In thecase of likelihood, the relationship 410 can carry a statistical valuequalified by different attributes. For example, A might be related to Bwith a probability of X based on the qualification (a patient is male,over 55 years old, and other attributes).

The likelihood or probability distribution for each of the relationships410, or for combinations of the relationships 410, may be calculatedbased on statistical information from a population of patients and fromclinical tests. These probability distributions are further enhanced bypatient characteristics. In certain example embodiments, the probabilitymay be calculated for any individual patient.

FIG. 5 illustrates a sample screen for a metadata library, in accordancewith an example embodiment. The metadata library 500 may includewell-structured medical metadata obtained by means of patternrecognition of natural language associated with medical knowledge. Thestructured medical metadata may include elements, any relationships thatmay exist between the elements of the medical metadata, and the mannerin which the elements can reference each other. In addition, themetadata library 500 may include a number of metadata dictionaries withequivalence classes of medical terms and phrases, so that the elementsof the medical metadata that are similar can be treated in a similarmanner.

A metadata dictionary may include an equivalence class of terms andphrases associated with a medical lexicon. The equivalency may beimportant because different standards have different terminologies todescribe the same thing. In medication, for example, most patients mighthave heard of “Nexium” but most physicians probably use “esomeprazole.”The metadata dictionary likely exists somewhere and would simply need tobe imported, be it from NIH, NICE, SNOWMED, or the index of medicaltextbooks.

In certain example embodiments, the metadata library 500 may be addedwith pattern recognition functionality. The pattern recognition may usean equivalence class dictionary of terms and phrases that provides anexhaustive cover of the medical lexicon. Across standards, languages,and differing nomenclatures, two (or more) words or phrases describingthe same thing may be placed into the same equivalence class and treatedas a single entity. This is important because patients often do not usethe same terminology as doctors do, yet the two groups must communicateto achieve an understanding of symptoms and diseases.

In certain example embodiments, the metadata library 500 may be builtusing a standard set of medical libraries such as Unified MedicalLanguage System (UMLS), SNOWMED CT, and the like.

The metadata library 500 may be constructed with a list of diseases, aset of symptoms that may be observed in a patient suffering from thediseases, a list of tests and tools to confirm or reject a disease as adiagnosis, known relationships between triggers or causes of diseases,such as environmental factors, genetic history, postal codes, workplacefactors, patient age/sex/medical history, and the like. The metadatalibrary 500 may additionally comprise relationships to other diseasesand conditions, complete with treatment options.

FIG. 5 illustrates a simplified example for the construction of one(symptoms and causes) of the binary relationships for asthma. A staticmedical metadata and phrase library is used. In this example, patientinputs 505 are coughing and wheezing. The first parser identified twodiseases 510: asthma and acid reflux and retrieved all the causes andsymptoms 515 associated with the diseases.

The metadata library 500 used may include the following examples:

Disease Metadata: asthma, acid reflux, GERD;

Context Metadata: signs and symptoms, causes, diagnosis, management,treatment;

Symptom Metadata: coughing, wheezing, chest tightness, shortness ofbreath, heartburn, regurgitation, trouble swallowing, dysphasia, sorethroat, odynophagia, pain with swallowing, nausea, chest pain, globus(pharingeus, hystericus), lump in throat;

Causes Metadata: low air quality, allergens, air pollution,environmental chemicals, smoking during pregnancy, formaldehydeexposure, endotoxin exposure, phthalates in PVC, viral respiratoryinfections, genetic, history of atopic disease, eczema, hay fever,Churg-Strauss syndrome, urticarial, vasculitis, beta blocker,psychological stress, genetic, GERD, obstructive sleep apnea, obesity,rhinosinusitis, Hiatal hernia, Zollinger-Ellison syndrome, hyperkalemia,scleroderma, systemic sclerosis, prednisolone, gallstones,Visceroptosis/Glenard syndrome;

Testing Metadata: spirometry, single-breath diffusing capacity, peakexpiratory flow rate, esophageal pH monitoring, barium swallow X-rays,esophageal manometry, esophagogastroduodenoscopy (EGD), short-termtreatment with proton-pump inhibitors;

Treatment Metadata: salbutamol (albuterol USAN), ipratropium bromide,anticholinergic bronchodilators, corticosteroids, beta-adrenoceptoragonists, leukotriene antagonists, oxygen, magnesium sulfate, heliox,Intravenous salbutamol, methylxanthines, ketamine, diet, sleeping on theleft side, antibiotics, proton-pump inhibitors, PPIs, omeprazole,esomeprazole, pantoprazole, lansoprazole, rabeprazole, gastric H2receptor blockers, ranitidine, famotidine, cimetidine, antacids, alginicacid, gaviscon, reglan, metoclopramide, prokinetic, sucralfate,carafate, mosapride citrate, 5-HT4 receptor agonist, baclofen, agonistof GABAB receptor, nissen fundoplication, transoral incisionlessfundoplication.

Using the metadata library 500 allows improving the quality andeffectiveness of personal healthcare, by introducing efficient andintelligent management of patients, while improving the overall qualityof care and reducing risks related to incorrect diagnosis, insufficientor excessive tests or treatments, and so forth.

Unlike conventional medical decision support systems, the system forautomated medical decision-making 200 provides true integration, fromclinical findings to diagnosis to treatment to prognosis and beyond. Theintegration covers at least a parser, inferential/diagnostic/learningcomponents, decision frameworks, and valuation models. Properintegration of these components may bridge the gap between medicalinformation and medical artificial intelligence.

FIG. 6 illustrates a process 600 of decision making using the system forautomated medical decision-making 200. A decision framework of thesystem for automated medical decision-making 200 consumes output from aninference engine and checks whether the diagnosis is definitive 605.Testing options to support the diagnosis may be evaluated and a test maybe ordered 610 or not 620 considering the cost of the test. Furthermore,treatment alternatives may be evaluated 615 in terms of efficacy,predicted prognosis, and so forth. Additionally, outcomes of a selectedtreatment alternative X 630 may be evaluated using multiple criteria bya multi-attribute utility 625 and based on observations 635. Thedecision framework may screen first for high risk situations (e.g.,life-threatening) and low risk contexts (e.g., alarm system).

In some embodiments, the decision framework may capture and representinformation about patient, doctor, or medical organization preferences,estimated costs for alternative treatment options, and further relatedinformation. Such information allows introduction of further aspects inthe decision process.

The system for automated medical decision-making 200 brings a valuationcapability to its architecture and design. The health status of apatient is presumed to be multi-attributed; therefore, the system 200characterizes or describes multi-attribute health states as y=(y₁, . . ., y_(n)). For example, a two-attribute case, y=(y₁; y₂), may beconsidered for evaluation treatment options for ovarian cancer. In thiscase, y₁ may be identically equal to radiation side-effects, and y₂ maybe identically equal to fertility.

The system for automated medical decision-making 200 is directed atend-to-end patient management to embrace patient management lifecycle(symptoms, diagnostics, testing, treatment plan, anticipated prognosis,and patient tracking), natural language parsing of patient symptoms(almost instantaneous machine interpretation of patient statements andcase history), and sophisticated causal mapping of relevant medicalknowledge and information (intelligence may represent relevantsubstrates). These elements can be considered an intelligentinfrastructure (absent any data) onto which medical information can bemapped and learned. A patient substrate is defined as a sub-net of thearchitecture, together with probabilistic characterizations oflikelihoods associated with individual nodes and paths in the network.Using the system for automated medical decision-making 200, completeelectronic handling of patients may be implemented, including consumingvoice, text, and laboratory data, performing diagnostics, automaticallyfilling out template forms, having the control and audit mechanisms inplace, statistical and other risk based inference models that mitigaterisk to patient, doctor, insurer, and so forth.

FIG. 7 illustrates creating patient substrate 700 using the system forautomated medical decision-making 200. Using a phrase library 705,medical information sources 205, and medical terminology systems (e.g.,UMLS) 735, the first parser 215 may provide medical information andmetadata 730 to the intelligent medical database 105. The intelligentmedical database 105 may structure the medical data 720 to formstructured medical metadata in intelligent medical database 105 andeventually create a casual network. Patient data may be received, forexample, from EMR 755 and stored in a statistical database 750. Amapping engine 740 may map patient data to the causal network 240 toreceive the patient substrate.

FIG. 8 illustrates a process 800 of finding a match 825 based onsymptoms and evidences 815. Parsers 820 provide medical data from ametadata database 845 and symptoms and evidences 815. Using the medicaldata, a match is determined in a universal intelligent network 805 bytraversing the network 810, and corresponding information is receivedfrom an ordered list of diseases, states, and outcomes 830. Furtherinformation associated with the match 825 may be received from theintelligent medical database 105 as well as statistical and otherdatabases 855.

In more detail, this procedure is shown by FIG. 9. Procedure 900includes receiving symptoms and evidence 915 by parsers 905. The parsers905 may additionally receive related information from artificialintelligence database 945 and intelligent medical database 105. Further,symptoms and evidence 915 may be matched with diseases, states, andoutcomes using the statistical database 955 to obtain a matched list925. The matched list 925 may be ordered by probability 930 to obtain anordered list 935, which is dropped into a causal network to find causalrelationships 960. Entry points for items in the causal network may befound 965. Then, the causal network may be traversed 970 and a sub-netmay be built 975.

FIG. 10 illustrates a diagrammatic representation of an example machinein the form of a computer system within which a set of instructions forcausing the machine to perform any one or more of the methodologiesdiscussed herein is executed. A computer system 1000 may include a setof instructions for causing the machine to perform any one or more ofthe methodologies discussed herein may be executed. In various exampleembodiments, the machine operates as a standalone device or may beconnected (e.g., networked) to other machines. In a networkeddeployment, the machine may operate in the capacity of a server or aclient machine in a server-client network environment, or as a peermachine in a peer-to-peer (or distributed) network environment. Themachine may be a personal computer (PC), a tablet PC, a set-top box(STB), a tablet computer, a cellular telephone, a smartphone, a portablemusic player (e.g., a portable hard drive audio device such as a MovingPicture Experts Group Audio Layer 3 (MP3) player), a web appliance, anetwork router, switch or bridge, or any machine capable of executing aset of instructions (sequential or otherwise) that specify actions to betaken by that machine. Further, while only a single machine isillustrated, the term “machine” shall also be taken to include anycollection of machines that individually or jointly execute a set (ormultiple sets) of instructions to perform any one or more of themethodologies discussed herein.

The example computer system 1000 includes a processor or multipleprocessors 1002 (e.g., a central processing unit (CPU), a graphicsprocessing unit (GPU), or both), a main memory 1004, and a static memory1006, which communicate with each other via a bus 1008. The computersystem 1000 may further include a video display unit 1010 (e.g., aliquid crystal display (LCD) or a cathode ray tube (CRT)). The computersystem 1000 may also include an alphanumeric input device 1012 (e.g., akeyboard), a cursor control device 1014 (e.g., a mouse), a disk driveunit 1016, a signal generation device 1018 (e.g., a speaker), and anetwork interface device 1020.

The disk drive unit 1016 includes a computer-readable medium 1022, onwhich is stored one or more sets of instructions and data structures(e.g., instructions 1024) embodying or utilized by any one or more ofthe methodologies or functions described herein. The instructions 1024may also reside, completely or at least partially, within the mainmemory 1004 and/or within the processors 1002 during execution thereofby the computer system 1000. The main memory 1004 and the processors1002 may also constitute machine-readable media.

The instructions 1024 may further be transmitted or received over anetwork 1026 via the network interface device 1020 utilizing any one ofa number of well-known transfer protocols (e.g., HyperText TransferProtocol (HTTP)).

While the computer-readable medium 1022 is shown in an exampleembodiment to be a single medium, the term “computer-readable medium”should be taken to include a single medium or multiple media (e.g., acentralized or distributed database and/or associated caches andservers) that store the one or more sets of instructions. The term“computer-readable medium” shall also be taken to include any mediumthat is capable of storing, encoding, or carrying a set of instructionsfor execution by the machine and that causes the machine to perform anyone or more of the methodologies of the present application, or that iscapable of storing, encoding, or carrying data structures utilized by orassociated with such a set of instructions. The term “computer-readablemedium” shall accordingly be taken to include, but not be limited to,solid-state memories, optical and magnetic media, and carrier wavesignals. Such media may also include, without limitation, hard disks,floppy disks, flash memory cards, digital video disks, random accessmemory (RAM), read only memory (ROM), and the like.

The example embodiments described herein may be implemented in anoperating environment comprising software installed on a computer, inhardware, or in a combination of software and hardware.

Thus, systems and methods for automated medical decision-making havebeen described. Although embodiments have been described with referenceto specific example embodiments, it will be evident that variousmodifications and changes may be made to these embodiments withoutdeparting from the broader spirit and scope of the system and methoddescribed herein. Accordingly, the specification and drawings are to beregarded in an illustrative rather than a restrictive sense.

1. A computer-implemented method for automated medical decision-making,the method comprising: parsing, by a first parser, text associated withone or more medical information sources to obtain medical information;structuring, by a processor, the medical information to form structuredmedical metadata in an intelligent medical database, wherein thestructured medical metadata comprises elements and relationships betweenthe elements; based on the structured medical metadata, creating, by theprocessor, a causal network, wherein the causal network includes atopological space with nodes and links between the nodes, the nodesrepresenting the elements of the structured medical metadata and thelinks representing the relationships between the elements; receiving, bythe processor, patient data from one or more patient data sources;parsing, by a second parser, the patient data to obtain processedpatient data; mapping, by the processor, the processed patient dataagainst the causal network; and based on the mapping, generating amedical decision for the patient.
 2. The method of claim 1, wherein theelements of the structured medical metadata include one or more of thefollowing: a disease, a state, a cause, a symptom, a treatment, a test,an effect, an outcome, and an outlier.
 3. (canceled)
 4. The method ofclaim 1, further comprising: weighting statistically, based on thepatient data, the relationships between the elements of the structuredmedical metadata.
 5. The method of claim 1, further comprising providingautomated medical decision-making.
 6. The method of claim 1, wherein theobtaining of the medical information from the one or more medicalinformation sources includes using one or more of the following: ametadata dictionary and a pattern recognition.
 7. The method of claim 1,further comprising: processing the patient data using an inference modeland a pattern recognition.
 8. The method of claim 1, further comprising:retrieving, interpreting, and collating, by one or more grammarindependent natural language parsers, the medical information from theone or more medical information sources.
 9. A system for automatedmedical decision-making, the system comprising: a first parserconfigured to parse text associated with one or more medical informationsources to obtain medical information; a second parser configured toparse patient data to obtain processed patient data; and a processor incommunication with the first parser and the second parser, the processorbeing configured to: structure the medical information to formstructured medical metadata in an intelligent medical database, whereinthe structured medical metadata comprises elements and relationshipsbetween the elements; create, based on the structured medical metadata,a causal network, wherein the causal network includes a topologicalspace with nodes and links between the nodes, the nodes representing theelements of the structured medical metadata and the links representingthe relationships between the elements; receive the patient data fromone or more patient data sources; map the processed patient data againstthe causal network; and generate, based on the mapping, a medicaldecision for the patient.
 10. The system of claim 9, wherein theelements of the structured medical metadata include one or more of thefollowing: a disease, a state, a cause, a symptom, a treatment, a test,an effect, an outcome, and an outlier.
 11. (canceled)
 12. The system ofclaim 9, wherein the relationships between the elements of thestructured medical metadata are weighted statistically based on thepatient data.
 13. The system of claim 9, wherein the first parser andthe second parser include one or more grammar independent naturallanguage parsers, the one or more grammar independent natural languageparsers configured to retrieve, interpret, and collate the medicalinformation from the one or more medical information sources.
 14. Thesystem of claim 9, wherein the medical information is obtained from theone or more medical information sources using one or more of thefollowing: a metadata dictionary and a pattern recognition.
 15. Thesystem of claim 14, wherein the metadata dictionary includes anequivalence class of terms and phrases associated with a medicallexicon.
 16. The system of claim 14, wherein the metadata dictionary iscaptured using a bioinformatics ontology.
 17. The system of claim 9,wherein the patient data is processed using an inference model and apattern recognition.
 18. The system of claim 9, wherein the patient dataincludes patient input in a form of a natural language.
 19. The systemof claim 9, wherein the patient data includes a text or a speechconverted into text.
 20. A system for automated medical decision-making,the system comprising: a first parser configured to parse textassociated with one or more medical information sources to obtainmedical information; a second parser configured to parse patient data toobtain processed patient data; and a processor in communication with thefirst parser and the second parser, the processor being configured to:structure the medical information to form structured medical metadata inan intelligent medical database, wherein the structured medical metadatacomprises elements and relationships between the elements, wherein theelements of the structured medical metadata include one or more of thefollowing: a disease, a state, a cause, a symptom, a treatment, a test,an effect, an outcome, and an outlier; create, based on the structuredmedical metadata, a causal network, wherein the causal network includesa topological space with nodes and links between the nodes, wherein thenodes represent the elements of the structured medical metadata and thelinks represent the relationships between the elements, therelationships between the elements of the structured medical metadatabeing weighted statistically based on the patient data; receive thepatient data from one or more patient data sources; map the processedpatient data against the causal network; and generate, based on themapping, a medical decision for the patient.